# A script file to produce plots and analyze columns 1. Source, 2. Opening Revenue, 3. Production budget, youtube viewcount
# By Chris Chiang

# Dependencies
import scipy.stats as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt


# read in csv file for title lis
movie_csv = "../data_files/final.csv"

# read in csv file for title list
movie_df = pd.read_csv(movie_csv)
movie_df.head()

	Released	Title	Source	Production Budget	Opening Weekend Revenue	Domestic Box Office	Infl. Adj. Dom. Box Office	International Box Office	Worldwide Box Office	ratings	...	nominations	trailer ids	max viewcount	Infl. Adj. Production Budget	Infl. Adj. International Box Office	Infl. Adj. Worldwide Box Office	Infl. Adj. Opening Weekend Revenue	Genre	release_year	release_month
0	2000-11-17 00:00:00+00:00	How the Grinch Stole Christmas	Based on Fiction Book/Short Story	123000000	55820330	260044825	438980791	85096578	345141403	[{'Source': 'Internet Movie Database', 'Value'...	...	37.0	['myTaigPrbsg']	3226963.0	207635884	143651245	582632036	94230110	Family	2000	11
1	2000-12-22 00:00:00+00:00	Cast Away	Original Screenplay	85000000	28883406	233632142	384884272	193598374	427230516	[{'Source': 'Internet Movie Database', 'Value'...	...	35.0	['4olwbrY2kwE', 'yY9Nm4RF-K0']	598437.0	140028520	318932868	703817140	47582360	Adventure	2000	12
2	2000-05-24 00:00:00+00:00	Mission: Impossible II	Based on TV	120000000	57845297	215409889	364078672	334178627	549588516	[{'Source': 'Internet Movie Database', 'Value'...	...	20.0	['vIpqpRuGrq4']	1171309.0	202820032	564817665	928896337	97768208	Adventure	2000	5
3	2000-05-05 00:00:00+00:00	Gladiator	Original Screenplay	103000000	34819017	187683805	317130387	270000000	457683805	[{'Source': 'Internet Movie Database', 'Value'...	...	104.0	['owK1qxDselE', 'AxQajgTyLcM']	2259163.0	174039682	456220527	773350914	58833889	Action	2000	5
4	2000-12-15 00:00:00+00:00	What Women Want	Original Screenplay	65000000	33614543	182805123	303565725	191300000	374105123	[{'Source': 'Internet Movie Database', 'Value'...	...	8.0	['VFwHs7fEUNs']	1313029.0	107938835	317672296	621238021	55820225	Comedy	2000	12

5 rows × 28 columns

sources=movie_df['Source']
source_gb = movie_df.groupby('Source')
colors = [f'C{x}' for x in range(len(sources.value_counts().index))]
plt.figure(figsize=(8,8))
plt.bar(sources.value_counts().index,sources.value_counts(),color=colors)
plt.xticks(rotation=90)
sources.value_counts()

Original Screenplay                    854
Based on Fiction Book/Short Story      371
Remake                                 113
Based on Comic/Graphic Novel           105
Based on Real Life Events               91
Based on TV                             76
Based on Factual Book/Article           62
Based on Folk Tale/Legend/Fairytale     29
Based on Game                           21
Based on Play                           19
Spin-Off                                18
Based on Musical or Opera                7
Based on Short Film                      7
Based on Theme Park Ride                 7
Based on Religious Text                  7
Based on Toy                             6
Based on Movie                           2
Based on Web Series                      1
Name: Source, dtype: int64

movie_df.columns

Index(['Released', 'Title', 'Source', 'Production Budget',
       'Opening Weekend Revenue', 'Domestic Box Office',
       'Infl. Adj. Dom. Box Office', 'International Box Office',
       'Worldwide Box Office', 'ratings', 'awards', 'rated', 'runtime',
       'genere', 'metascore', 'imdbRating', 'imdbVotes', 'imdbID',
       'nominations', 'trailer ids', 'max viewcount',
       'Infl. Adj. Production Budget', 'Infl. Adj. International Box Office',
       'Infl. Adj. Worldwide Box Office', 'Infl. Adj. Opening Weekend Revenue',
       'Genre', 'release_year', 'release_month'],
      dtype='object')

# Sort by box office/return
dom_sort=movie_df.sort_values('Infl. Adj. Dom. Box Office',ascending=False)
world_sort=movie_df.sort_values('Infl. Adj. Worldwide Box Office',ascending=False)
movie_df['Return world adj']=movie_df['Infl. Adj. Worldwide Box Office']-movie_df['Infl. Adj. Production Budget']
movie_df['Return world per']=(movie_df['Infl. Adj. Worldwide Box Office'])/movie_df['Infl. Adj. Production Budget']*100
return_sort=movie_df.sort_values('Return world per',ascending=False)
movie_df['Infl. Adj. Dom. Box Office']=movie_df['Infl. Adj. Dom. Box Office']/1000000
movie_df['Infl. Adj. International Box Office']=movie_df['Infl. Adj. International Box Office']/1000000
movie_df['Infl. Adj. Worldwide Box Office']=movie_df['Infl. Adj. Worldwide Box Office']/1000000
movie_df['Infl. Adj. Production Budget']=movie_df['Infl. Adj. Production Budget']/1000000
movie_df['Infl. Adj. Opening Weekend Revenue']=movie_df['Infl. Adj. Opening Weekend Revenue']/1000000

pie_series = dom_sort.iloc[0:500]['Source'].value_counts().loc[dom_sort.iloc[0:500]['Source'].value_counts() > 10]
pie_series['Other']=dom_sort.iloc[0:500]['Source'].value_counts().loc[dom_sort.iloc[0:500]['Source'].value_counts() < 10].sum()
pie_series.plot.pie(subplots=True, autopct="%1.1f%%", shadow=True, startangle=45, title='Source of Top 500 Adjusted Domestic Box office Success')
plt.savefig('../plot_pics/Source1.png')

pie_series = world_sort.iloc[0:500]['Source'].value_counts().loc[world_sort.iloc[0:500]['Source'].value_counts() > 10]
pie_series['Other']=world_sort.iloc[0:500]['Source'].value_counts().loc[world_sort.iloc[0:500]['Source'].value_counts() < 10].sum()
pie_series.plot.pie(subplots=True, autopct="%1.1f%%", shadow=True, startangle=45, title='Source of Top 500 Adjusted Worldwide Box Office Success')
plt.savefig('../plot_pics/Source2.png')

pie_series = return_sort.iloc[0:500]['Source'].value_counts().loc[return_sort.iloc[0:500]['Source'].value_counts() > 10]
pie_series['Other']=return_sort.iloc[0:500]['Source'].value_counts().loc[return_sort.iloc[0:500]['Source'].value_counts() < 10].sum()
pie_series.plot.pie(subplots=True, autopct="%1.1f%%", shadow=True, startangle=45, title='Source of Top 500 adjusted Percetage Return')
plt.savefig('../plot_pics/Source3.png')

pie_series = return_sort.iloc[0:100]['Source'].value_counts().loc[return_sort.iloc[0:100]['Source'].value_counts() > 3]
pie_series['Other']=return_sort.iloc[0:100]['Source'].value_counts().loc[return_sort.iloc[0:100]['Source'].value_counts() < 3].sum()
pie_series.plot.pie(subplots=True, autopct="%1.1f%%", shadow=True, startangle=45, title='Source of Top 100 Movies with Highest Worldwide Return')
plt.savefig('../plot_pics/Source4.png')

y=source_gb['Infl. Adj. Worldwide Box Office'].mean()
exclude_low=movie_df['Source'].value_counts()>10
colors = [f'C{x}' for x in range(len(exclude_low))]
y.loc[exclude_low].plot.bar(colors=colors,figsize=(8,5))
plt.title("Average Worldwide Box Office of Movie Sort by Source (excluding small groups) ")
plt.xlabel("Movie Source")
plt.ylabel("Average Worldwide BoxOffice in Millions of Dollars Adjusted for Inflation")
plt.ylim(50,500)
plt.savefig('../plot_pics/Source5.png',pad_inches=(8,5),dpi=500)

y=source_gb['Return world per'].mean()
exclude_low=movie_df['Source'].value_counts()>10
colors = [f'C{x}' for x in range(len(exclude_low))]
y.loc[exclude_low].plot.bar(colors=colors,figsize=(8,5))
plt.title("Average Worldwide Return of Movie Sort by Source (excluding small groups) ") #less than 10 movies
plt.xlabel("Movie Source")
plt.ylabel("Average Worldwide Return in Percent of Production Budget Adjusted for Inflation")

plt.ylim(250,400)
plt.savefig('../plot_pics/Source6.png')

# conclusion data for sequls??
# Og not bad too broad star wars, avator, indian jones
# Spin off, comic
# base on tv, fiction
# non fiction, smaller but safe bet
# disney fairtale, mediocore
# no remake, game
# religious movie very high but 7 m

y=source_gb['Infl. Adj. Worldwide Box Office'].median()/1000000
exclude_low=movie_df['Source'].value_counts()>10
colors = [f'C{x}' for x in range(len(exclude_low))]
y.loc[exclude_low].plot.bar(colors=colors,figsize=(8,5))
plt.title("Median Worldwide Box Office of Movie Sort by Source (excluding small groups) ")
plt.xlabel("Movie Source")
plt.ylabel("Average Worldwide BoxOffice in Millions of Dollars Adjusted for Inflation")
plt.ylim(50,400)
plt.savefig('../plot_pics/Source7.png')

y=source_gb['Return world per'].median()
exclude_low=movie_df['Source'].value_counts()>10
colors = [f'C{x}' for x in range(len(exclude_low))]
y.loc[exclude_low].plot.bar(colors=colors,figsize=(8,5))
plt.title("Median Worldwide Return of Movie Sort by Source (excluding small groups) ") #less than 10 movies
plt.xlabel("Movie Source")
plt.ylabel("Average Worldwide Return in Percent of Production Budget Adjusted for Inflation")

plt.ylim(200,300)
plt.savefig('../plot_pics/Source8.png')

def line_plot(x, y):
    # Function that takes in 2 panda series x, y and plots a linear regression line
    slope, inter, rval, pval, stderr = st.linregress(x, y)
    print(f'The r-squared is : {rval ** 2}')
    x_line = np.arange(x.min(), x.max()+1, ((x.max()-x.min())/100))
    y_line = (slope * x_line) + inter
    plt.plot(x_line, y_line, 'r',
             label='y = {0:.3f}x + {1:.3f}, r-square ={2:.2f}'.format(slope, inter,rval ** 2))
    # Using legned label to annotate because it can use best location
    plt.legend(loc='best')

movie_df.plot.scatter('Infl. Adj. Dom. Box Office','Infl. Adj. International Box Office')
line_plot(movie_df['Infl. Adj. Dom. Box Office'],movie_df['Infl. Adj. International Box Office'])
plt.title("Domestic Vs Internationl Box Office Adjusted")
plt.xlabel("Domestic Boxoffice in millions of dollars")
plt.ylabel("International Boxoffice in millions of dollars")
plt.savefig('../plot_pics/DomVSInter.png')

The r-squared is : 0.6879120021651185

movie_df.plot.scatter('Infl. Adj. Opening Weekend Revenue','Infl. Adj. Worldwide Box Office')
line_plot(movie_df['Infl. Adj. Opening Weekend Revenue'],movie_df['Infl. Adj. Worldwide Box Office'])

plt.title("Domestic Opening Weekend Revenue Vs Worldwide Box Office Adjusted")
plt.xlabel("Domestic Opening Weekend Revenue Boxoffice in millions")
plt.ylabel("Worldwide Boxoffice in millions")
plt.savefig('../plot_pics/openVSworld.png')

# 'Opening Weekend Revenue' # domestic

The r-squared is : 0.7217316088386965

movie_df.plot.scatter('Infl. Adj. Opening Weekend Revenue','Infl. Adj. Dom. Box Office',colors='C2')
line_plot(movie_df['Infl. Adj. Opening Weekend Revenue'],movie_df['Infl. Adj. Dom. Box Office'])
plt.title("Domestic Opening Weekend Revenue Vs Domestic Box Office Adjusted")
plt.xlabel("Domestic Opening Weekend Revenue Boxoffice in millions")
plt.ylabel("Domestic Boxoffice in millions")
plt.savefig('../plot_pics/openVSdom.png')

The r-squared is : 0.7942682369028335

movie_df['Infl. Adj. Opening Weekend Revenue'].plot.box()
q1, q2, q3 = movie_df['Infl. Adj. Opening Weekend Revenue'].quantile([.25, .5, .75])
IQR = q3 - q1
lower_limit = q1 - (1.5 * IQR)
upper_limit = q3 + (1.5 * IQR)
upper_limit
plt.title("Domestic Opening Weekend Revenue Box Plot")
plt.ylabel("Domestic Opening Weekend Revenue Boxoffice in millions")
plt.savefig('../plot_pics/openbox.png')

no_open_out = movie_df.loc[movie_df['Infl. Adj. Opening Weekend Revenue']<upper_limit]
no_open_out.head()

	Released	Title	Source	Production Budget	Opening Weekend Revenue	Domestic Box Office	Infl. Adj. Dom. Box Office	International Box Office	Worldwide Box Office	ratings	...	max viewcount	Infl. Adj. Production Budget	Infl. Adj. International Box Office	Infl. Adj. Worldwide Box Office	Infl. Adj. Opening Weekend Revenue	Genre	release_year	release_month	Return world adj	Return world per
1	2000-12-22 00:00:00+00:00	Cast Away	Original Screenplay	85000000	28883406	233632142	384.884272	193598374	427230516	[{'Source': 'Internet Movie Database', 'Value'...	...	599674.0	140.028520	318.932868	703.817140	47.582360	Adventure	2000	12	563788620	502.624137
3	2000-05-05 00:00:00+00:00	Gladiator	Original Screenplay	103000000	34819017	187683805	317.130387	270000000	457683805	[{'Source': 'Internet Movie Database', 'Value'...	...	2262657.0	174.039682	456.220527	773.350914	58.833889	Action	2000	5	599311232	444.353210
4	2000-12-15 00:00:00+00:00	What Women Want	Original Screenplay	65000000	33614543	182805123	303.565725	191300000	374105123	[{'Source': 'Internet Movie Database', 'Value'...	...	1315229.0	107.938835	317.672296	621.238021	55.820225	Comedy	2000	12	513299186	575.546346
5	2000-06-30 00:00:00+00:00	The Perfect Storm	Based on Factual Book/Article	120000000	41325042	182618434	308.655646	146093000	328711434	[{'Source': 'Internet Movie Database', 'Value'...	...	87080.0	202.820036	246.921563	555.577209	69.846221	Drama	2000	6	352757173	273.926196
6	2000-10-06 00:00:00+00:00	Meet the Parents	Original Screenplay	55000000	28623300	166225040	280.553027	164200000	330425040	[{'Source': 'Internet Movie Database', 'Value'...	...	17461.0	92.828472	277.135183	557.688210	48.310130	Comedy	2000	10	464859738	600.772800

5 rows × 30 columns

no_open_out.plot.scatter('Infl. Adj. Opening Weekend Revenue','Infl. Adj. Worldwide Box Office')
line_plot(no_open_out['Infl. Adj. Opening Weekend Revenue'],no_open_out['Infl. Adj. Worldwide Box Office'])

The r-squared is : 0.4754415362994705

no_open_out.plot.scatter('Infl. Adj. Opening Weekend Revenue','Infl. Adj. Dom. Box Office')
line_plot(no_open_out['Infl. Adj. Opening Weekend Revenue'],no_open_out['Infl. Adj. Dom. Box Office'])

The r-squared is : 0.5845810466301934

movie_df.plot.scatter('Infl. Adj. Opening Weekend Revenue','Return world per',colors='C7')
line_plot(movie_df['Infl. Adj. Opening Weekend Revenue'],movie_df['Return world per'])

plt.title("Domestic Opening Weekend Vs Worldwide Box Office Return")
plt.xlabel("Domestic Opening Weekend Revenue Boxoffice in millions")
plt.ylabel("BoxOffice Return in percent of Production Budget")
plt.savefig('../plot_pics/openVSre.png')

The r-squared is : 0.1537259524914579

no_open_out.plot.scatter('Infl. Adj. Opening Weekend Revenue','Return world per',colors='C9')
line_plot(no_open_out['Infl. Adj. Opening Weekend Revenue'],no_open_out['Return world per'])

plt.title("Domestic Opening Weekend Vs Worldwide Box Office Return no x outlier")
plt.xlabel("Domestic Opening Weekend Revenue Boxoffice in millions")
plt.ylabel("BoxOffice Return in percent of Production Budget")
plt.savefig('../plot_pics/openVSre_out1.png')

The r-squared is : 0.07314428923217578

movie_df['Return world per'].plot.box()
q1, q2, q3 = movie_df['Return world per'].quantile([.25, .5, .75])
IQR = q3 - q1
lower_limit = q1 - (1.5 * IQR)
upper_limit = q3 + (1.5 * IQR)
upper_limit
no_open_out_return_out = no_open_out.loc[no_open_out['Return world per']<upper_limit]
no_open_out_return_out.head()
no_return_out = movie_df.loc[movie_df['Return world per']<upper_limit]
plt.title("Worldwide Return Box Plot")
plt.ylabel("Worldwide Return in percent of budget")
plt.savefig('../plot_pics/returnbox.png')

no_open_out_return_out.plot.scatter('Infl. Adj. Opening Weekend Revenue','Return world per', colors='C4')
line_plot(no_open_out_return_out['Infl. Adj. Opening Weekend Revenue'],no_open_out_return_out['Return world per'])
# no return coclusion
# big movie, big open, big gross
# world vs domes

plt.title("Domestic Opening Weekend Vs Worldwide Box Office Return no x, y outlier")
plt.xlabel("Domestic Opening Weekend Revenue Boxoffice in millions")
plt.ylabel("BoxOffice Return in percent of Production Budget")
plt.savefig('../plot_pics/openVSre_out2.png')

The r-squared is : 0.13267848950799233

movie_df.plot.scatter('Infl. Adj. Production Budget','Infl. Adj. Worldwide Box Office')
line_plot(movie_df['Infl. Adj. Production Budget'],movie_df['Infl. Adj. Worldwide Box Office'])

plt.title("Production Budget Vs Worldwide Box Office")
plt.xlabel("Production Budget in millions")
plt.ylabel("Worldwide Box Office in millions")
plt.savefig('../plot_pics/pro1.png')

# !!!

The r-squared is : 0.48168688474741156

movie_df.columns

Index(['Released', 'Title', 'Source', 'Production Budget',
       'Opening Weekend Revenue', 'Domestic Box Office',
       'Infl. Adj. Dom. Box Office', 'International Box Office',
       'Worldwide Box Office', 'ratings', 'awards', 'rated', 'runtime',
       'genere', 'metascore', 'imdbRating', 'imdbVotes', 'imdbID',
       'nominations', 'trailer ids', 'max viewcount',
       'Infl. Adj. Production Budget', 'Infl. Adj. International Box Office',
       'Infl. Adj. Worldwide Box Office', 'Infl. Adj. Opening Weekend Revenue',
       'Genre', 'release_year', 'release_month', 'Return world adj',
       'Return world per'],
      dtype='object')

movie_df.plot.scatter('Infl. Adj. Production Budget','Infl. Adj. Dom. Box Office')
line_plot(movie_df['Infl. Adj. Production Budget'],movie_df['Infl. Adj. Dom. Box Office'])

The r-squared is : 0.36019902429908407

movie_df.plot.scatter('Infl. Adj. Production Budget','Return world per',colors='C7')
line_plot(movie_df['Infl. Adj. Production Budget'],movie_df['Return world per'])

# !!!
plt.title("Production Budget Vs Worldwide Box Office Return")
plt.xlabel("Production Budget in millions")
plt.ylabel("BoxOffice Return in percent of Production Budget")
plt.savefig('../plot_pics/pro2.png')

The r-squared is : 0.0027429392795294033

no_return_out.plot.scatter('Production Budget','Return world per',colors='C9')
line_plot(no_return_out['Production Budget'],no_return_out['Return world per'])
# big budget weak big movie
# not smart return

# !!!
plt.title("Production Budget Vs Worldwide Box Office Return with no y outliers")
plt.xlabel("Production Budget in millions")
plt.ylabel("BoxOffice Return in percent of Production Budget")
plt.savefig('../plot_pics/pro3.png')

The r-squared is : 0.003532945556999656

you_csv = "../data_files/final_youtube_fix.csv"
you_df=pd.read_csv(you_csv)
you_df['max viewcount'].dropna()
movie_df['max viewcount']=you_df['max viewcount']
movie_df['max viewcount'].max()

105371718.0

youtube_df=you_df.dropna(subset=['max viewcount'])
youtube_df['Return world adj']=youtube_df['Infl. Adj. Worldwide Box Office']-youtube_df['Infl. Adj. Production Budget']
youtube_df['Return world per']=(youtube_df['Infl. Adj. Worldwide Box Office'])/youtube_df['Infl. Adj. Production Budget']*100
youtube_df['max viewcount']=youtube_df['max viewcount']/1000000

youtube_df.plot.scatter('max viewcount','Return world per',colors='C7')
line_plot(youtube_df['max viewcount'],youtube_df['Return world per'])
youtube_df['Infl. Adj. Worldwide Box Office']=youtube_df['Infl. Adj. Worldwide Box Office']*1000000

# !!!
plt.title("Trailer Views Vs Worldwide Box Office Return")
plt.xlabel("Trailer Views in millions")
plt.ylabel("BoxOffice Return in percent of Production Budget")
plt.savefig('../plot_pics/you1.png')

The r-squared is : 0.06346069893487452

youtube_df['Infl. Adj. Worldwide Box Office']=youtube_df['Infl. Adj. Worldwide Box Office']*1000000

q1, q2, q3 = youtube_df['Return world per'].quantile([.25, .5, .75])
IQR = q3 - q1
lower_limit = q1 - (1.5 * IQR)
upper_limit = q3 + (1.5 * IQR)
upper_limit
no_return_out = youtube_df.loc[youtube_df['Return world per']<upper_limit]

no_return_out.plot.scatter('max viewcount','Return world per',colors='C7')
line_plot(no_return_out['max viewcount'],no_return_out['Return world per'])

The r-squared is : 0.04206830311894089

youtube_df.plot.scatter('max viewcount','Infl. Adj. Dom. Box Office')
line_plot(youtube_df['max viewcount'],youtube_df['Infl. Adj. Dom. Box Office'])

The r-squared is : 0.13410553817208204

youtube_df.plot.scatter('max viewcount','Infl. Adj. Worldwide Box Office')
line_plot(youtube_df['max viewcount'],youtube_df['Infl. Adj. Worldwide Box Office'])

# !!!
plt.title("Trailer Views Vs Worldwide Box Office")
plt.xlabel("Trailer Views in millions")
plt.ylabel("Worldwide Box Office in millions")
plt.savefig('../plot_pics/you2.png')

The r-squared is : 0.17710139150581322

q1, q2, q3 = youtube_df['max viewcount'].quantile([.25, .5, .75])
IQR = q3 - q1
lower_limit = q1 - (1.5 * IQR)
upper_limit = q3 + (1.5 * IQR)
upper_limit
no_view_out = youtube_df.loc[youtube_df['max viewcount']<upper_limit]
no_view_out.plot.scatter('max viewcount','Infl. Adj. Worldwide Box Office')
line_plot(no_view_out['max viewcount'],no_view_out['Infl. Adj. Worldwide Box Office'])

# !!!
plt.title("Trailer Views Vs Worldwide Box Office no x outliers")
plt.xlabel("Trailer Views in millions")
plt.ylabel("Worldwide Box Office in millions")
plt.savefig('../plot_pics/you4.png')

The r-squared is : 0.009905525825446201

youtube_df['max viewcount'].plot.box()

# !!!
plt.title("Youtube Trailer Views Box Plot")
plt.ylabel("Trailer Views in millions")
plt.savefig('../plot_pics/you3.png')

youtube_2006=youtube_df.loc[youtube_df['release_year']<2005]
q1, q2, q3 = youtube_2006['max viewcount'].quantile([.25, .5, .75])
IQR = q3 - q1
lower_limit = q1 - (1.5 * IQR)
upper_limit = q3 + (1.5 * IQR)
upper_limit
youtube_2006_view_out=youtube_2006.loc[youtube_2006['max viewcount']<upper_limit]

youtube_2006.plot.scatter('max viewcount','Return world per')
line_plot(youtube_2006['max viewcount'],youtube_2006['Return world per'])

The r-squared is : 0.08467457579811932

youtube_2006.plot.scatter('max viewcount','Infl. Adj. Worldwide Box Office')
line_plot(youtube_2006['max viewcount'],youtube_2006['Infl. Adj. Worldwide Box Office'])
# !!!

plt.title("Trailer Views Vs Worldwide Box Office After 2006")
plt.xlabel("Trailer Views in millions")
plt.ylabel("Worldwide Box Office in millions")
plt.savefig('../plot_pics/you5.png')

The r-squared is : 0.10278089451518506

youtube_2006_view_out.plot.scatter('max viewcount','Infl. Adj. Worldwide Box Office')
line_plot(youtube_2006_view_out['max viewcount'],youtube_2006_view_out['Infl. Adj. Worldwide Box Office'])
# !!!

The r-squared is : 0.025259601899708314

youtube_2010=youtube_df.loc[youtube_df['release_year']<2010]
youtube_2010.plot.scatter('max viewcount','Infl. Adj. Worldwide Box Office')
line_plot(youtube_2010['max viewcount'],youtube_2010['Infl. Adj. Worldwide Box Office'])

The r-squared is : 0.046715774856232774